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Hyperspectral Image Vegetation Change Detection Based on Biochemical Parameters Inversion

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

Change detection of remote sensing images is a technology that one can get the change information by observing images of the same place obtained at different times. Hyperspectral remote sensing images can record detailed spectral information and reflect subtle differences between target and background. Hyperspectral change detection methods focus on changes between the different categories of feature, without fully taking the changes within the single ground type into account. In this paper, a hyperspectral vegetation change detection method based on biochemical parameters inversion is proposed. The change can be extracted from the vegetation biochemical parameters image by analyzing leaf water content, lignin content and other biochemical parameters. Experiments are conducted on both airborne and ground-based observation data. It shows that the change detection method based on biochemical parameters inversion reaches a high detection rate of 87.5% with a low false detection rate, which demonstrates superiority of the change detection methodology we proposed compared to other traditional methods.

Index Terms

Hyperspectral remote sensing image Vegetation biochemical parameters Vegetation index Change detection Component analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina

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